Introduction

Summary

platelib is an attempt to make common tasks when working with kinetic platereader, especially amyloid aggregation, data easy and compatible with Python.

Disclaimer

The state of this repository is one of very early development, the code is not elegant, there most likely are bugs so Use with caution!

Read the docs

A brief overview is given below, for more detailed information see the docs directory and subfolders, or even better have a look at the source :)

Installation

Prerequisites

• python
• pip
• git (optional)

Download

Either download the source distribution directly or use:

git https://github.com/edager/platelib

Install

Go to directory where the platelib source file is (platelib/dist/ if git was used) and run the following in the terminal:

pip install platelib-X.X.tar.gz

Where X.X should be replaced by the version that was downloaded.

Upgrade

Go to directory where the platelib source file is (platelib/dist/ if git was used) and run the following in the terminal:

pip install --upgrade platelib-X.X.tar.gz

Where X.X should be replaced by the version that was downloaded.

Uninstall

Simply go to the terminal and run:

pip uninstall platelib

Cookbook

Reading in data

The main functionality of platelib is the read_plate function that allows for reading in platereader data from kinetic experiments into a common framework namely into the Plate_data class. The following examples assumes for simplicity that platelib has been imported using:

from platelib import *

If an equal number of replicates per sample were prepared this can be specified (default is 3):

p = read_plate('path/to/file', replicates=5)              

It can be specified which direction the replicates were loaded onto the plate where 'hori' (horizontal) means towards increasing numbers and 'vert' is towards increasing letters (default is 'hori'):

p = read_plate('path/to/file', rep_direction='vert')

NOTE that the replicates have to be next to each other!

Alternatively it can be specified which wells contains replicates:

p = read_plate('path/to/file', named_samples=[['B03', 'D07'], ['B02', 'E06', 'G12']]     

Data from Tecan platereaders can be read in as (default is 'bmg'):

p = read_plate('path/to/file', platereader='tecan')

NOTE that this functionality has not been fully tested yet!

As well as from BMG platereaders either where the data has prior been transposed True such that well data are in column format or in row format False (default is True):

p = read_plate('path/to/file', transposed=False)

Note that it's automatically detected if several measurements (e.g.) were made per time-point (see Accessing data)

The time unit can also be specified which as either 'seconds', 'minutes', 'hours', or 'days' will carry along into indexes if exported and to unit of x-axis if plotted (default is 'hours'):

p = read_plate('path/to/file',time_unit='days')

Accessing data

The Plate_data class allows for different ways of accessing the data

Through index:

p[1]

Through index slice:

p[::3]

Through well name:

p['B02']  

Through list of well names:

p[['B02','C03','D04']] 

Retrieved as a pandas.DataFrame with wellnames as column names and time points as index:

df = Plate_data.to_a_dataframe()

Or as a (C)omma (S)eperated (V)aribles file with the first line being (time unit + ) well names and the first column are the time points:

Plate_data.to_a_csv('path/to/file.csv')

Plotting data

The data is plotted according to replicates, and subtitles can be added (default is None):

p.plot(titles=['condition 1', 'conditions 2'])

It can be specified whether all plots should have its own y-axis, whether all plots should have the same (default is True):

p.plot(sharey='False')

If several measurements were made per time-point it can be specified whether all measurements should be plotted or not (default is True):

p.plot(plot_multi='False')